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Spatiotemporal accuracy evaluation and errors analysis of global VTEC maps using a simulation technique / Jian Lin in GPS solutions, vol 27 n° 1 (January 2023)  

Public [article]  inGPS solutions > vol 27 n° 1 (January 2023) . - n° 6 Titre : Spatiotemporal accuracy evaluation and errors analysis of global VTEC maps using a simulation technique Type de document : Article/Communication Auteurs : Jian Lin, Auteur ; Xinxing Li, Auteur ; Shenfeng Gu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] données GPS [Termes IGN] harmonique sphérique [Termes IGN] modèle cartographique [Termes IGN] modèle ionosphérique [Termes IGN] phase [Termes IGN] rayonnement solaire [Termes IGN] simulation [Termes IGN] station GPS [Termes IGN] teneur verticale totale en électrons Résumé : (auteur) The computation of vertical total electron content (VTEC) maps has become an important issue gradually for the international GNSS service. Given the current literature reports, little research is involved in the quantitative analysis of each error of the VTEC map and the spatiotemporal characteristic of global VTEC accuracy. Based on the single layer model and sphere harmonic function, we propose an approach using simulated GPS data to comprehensively verify the accuracy of the VTEC map. The spatiotemporal characteristic of global VTEC accuracy and the errors induced by different processing steps, i.e., carrier phase to code leveling, mapping function (MF), DCB estimation and coefficient fitting, are analyzed and discussed in detail. In addition, the effect of solar activity on the accuracy of the global VTEC map, MF and DCB estimation has been discussed. The results suggest: First, it is found that the MF error at sunrise is more significant than that at sunset, and this important characteristic can be proven based on the analysis of theory and ionospheric radio occultation and VTEC measurements; second, the MF is the most significant error source in the VTEC processing for regions with dense and homogeneous distributed GPS stations, e.g., North America and Europe. The VTEC accuracy in these regions can be improved by 100% with the satellite elevation cutoff angle increasing from 12° to 30°; finally, compared with the global VTEC accuracy using 350 GPS stations observations, the accuracy is improved by 306% based on the double GPS stations with even distribution. Numéro de notice : A2023-002 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-022-01343-y Date de publication en ligne : 13/10/2022 En ligne : https://doi.org/10.1007/s10291-022-01343-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101871 [article]

Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)  

Public [article]  inEarth and space science > vol 9 n° 10 (October 2022) . - 8 p. Titre : Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity Type de document : Article/Communication Auteurs : Omid Memarian Sorkhabi, Auteur ; Muhammed Milani, Auteur ; Seyed Mehdi Seyed Alizadeh, Auteur Année de publication : 2022 Article en page(s) : 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] apprentissage profond [Termes IGN] champ de vitesse [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] géodynamique [Termes IGN] point géodésique [Termes IGN] positionnement par GPS [Termes IGN] station GPS [Termes IGN] tectonique Résumé : (auteur) Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated to estimate GV. In this research, four methods of convolutional neural networks (CNNs), deep Boltzmann machines, deep belief net and recurrent neural networks have been applied. The GV of 42 global positioning system stations is entered the deep learning methods. The outputs of the four methods have successfully passed the normality test. The results show that the CNN method has a lower goodness of fit and root mean square error (RMSE). CNN can learn different dependencies and extract features. Numéro de notice : A2022-757 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1029/2021EA002202 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1029/2021EA002202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101763 [article]

Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde / Mir Reza Ghaffari Razin in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 1 Titre : Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Samed Inyurt, Auteur Année de publication : 2022 Article en page(s) : n° 1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Inférence floue [Termes IGN] modélisation spatio-temporelle [Termes IGN] positionnement ponctuel précis [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] retard troposphérique zénithal [Termes IGN] station GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) Water vapor (WV) is one of the most important parameters in meteorological studies. Using an adaptive neuro-fuzzy inference system (ANFIS), a new method has been proposed for spatiotemporal modeling of precipitable WV (PWV). In a first step, the tropospheric zenith wet delay (ZWD) is calculated using the observations of 23 GPS stations in the northwest of Iran. Out of these 23 stations, 21 stations for training and 2 stations for testing and validating were selected. The observations are for 15 days, ranging from day of year (DOY) 300 to 314 in 2011. The reason for choosing this area and time interval is the availability of a complete set of data. Then, the values of ZWD are converted to PWV. The PWV values obtained from this step are considered as the output of the ANFIS. Also, the latitude and longitude values of the GPS stations, the DOY, observational time (min), temperature (T), pressure (P), and relative humidity (RH) are considered input to ANFIS. The ANFIS network is trained using the back-propagation algorithm. After the training step, the PWV values are evaluated at 2 test stations, KLBR and GGSH, and at Tabriz radiosonde station (38.08° N, 46.28°E). For a more accurate evaluation, all the results of the new method are compared with the voxel-based tomography model. The evaluation of the results is performed using the relative error, standard deviation, correlation coefficient, and root-mean-square error (RMSE). Also, precise point positioning (PPP) is used to better evaluate the proposed model at test stations. The value of the correlation coefficient at the radiosonde station for the ANFIS and voxel is 0.90 and 0.87, respectively. Also, the minimum RMSE calculated for the ANFIS and voxel are 1.02 and 1.06 mm, respectively. In the PPP analysis, an improvement of about 4 mm is observed in the coordinates of the test stations using ANFIS. The results confirm the capability and high accuracy of the proposed model in determining the temporal and spatial variations of PWV. Numéro de notice : A2022-003 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01184-1 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01184-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98828 [article]

A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers / N. Orouji in GPS solutions, vol 25 n° 3 (July 2021)  

Public [article]  inGPS solutions > vol 25 n° 3 (July 2021) . - Article 84 Titre : A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers Type de document : Article/Communication Auteurs : N. Orouji, Auteur ; M. R. Mosavi, Auteur Année de publication : 2021 Article en page(s) : Article 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] décalage d'horloge [Termes IGN] horloge du récepteur [Termes IGN] méthode robuste [Termes IGN] Perceptron multicouche [Termes IGN] précision des données [Termes IGN] récepteur GPS [Termes IGN] station GPS [Termes IGN] synchronisation Résumé : (Auteur) Accurate timing is one of the key features of the Global Positioning System (GPS), which is employed in many critical infrastructures. Any imprecise time measurement in GPS-based structures, such as smart power grids, economic activities, and communication towers, can lead to disastrous results. The vulnerability of the stationary GPS receivers to the time synchronization attacks (TSAs) jeopardizes the GPS timing precision and trust level. In the past few years, studies suggested the adoption of estimators to follow the authentic trend of the clock offset information under attack conditions. However, the estimators would lose track of the authentic signal without proper knowledge of the signal characteristics. Therefore, a multi-layer perceptron neural network (MLP NN) is proposed to follow the trend of the data. The main difference between the proposed method and typical estimators is the reliance of the network on the training information consisting of signal features. The proposed MLP NN performance has been evaluated through two real-world datasets and two well-known types of TSA. The root mean square error results exhibit an improvement of at least six times compared to other conventional and state-of-art methods. Numéro de notice : A2021-331 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01124-z Date de publication en ligne : 05/04/2021 En ligne : https://doi.org/10.1007/s10291-021-01124-z Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97501 [article]

Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3682 - 3694 Titre : Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data Type de document : Article/Communication Auteurs : Jia He, Auteur ; Zhizhao Liu, Auteur Année de publication : 2021 Article en page(s) : pp 3682 - 3694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amérique du nord [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] image Terra-MODIS [Termes IGN] modèle de régression [Termes IGN] modèle de transfert radiatif [Termes IGN] précision des données [Termes IGN] station GPS [Termes IGN] vapeur d'eau Résumé : (Auteur) A new algorithm of retrieving atmospheric water vapor from MODIS near-infrared (IR) (NIR) data by using a regression fitting method based on Global Positioning System (GPS)-derived water vapor is developed in this work. The algorithm has been used to retrieve total column water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) satellites both Terra and Aqua under cloud-free conditions from solar radiation in the NIR channels. Water vapor data estimated from GPS observations recorded from 2003 to 2017 by the SuomiNet GPS network over the western North America are used as ground truth references. The GPS stations were classified into six subsets based on the surface types adopted from MCD12Q1 IGBP legend. The differences in surface types are considered in the regression fitting procedure, thus different regression functions are trained for different surface types. Thus, the wet bias in the operational MODIS water vapor products has been significantly reduced. Water vapor retrieved from each of the three absorption channels and the weighted water vapor of combined three absorption channels are analyzed. Validation shows that the weighted water vapor performs better than the single-channel results. Compared to the MODIS/Terra water vapor products, the RMSE has been reduced by 50.78% to 2.229 mm using the two-channel ratio transmittance method and has been reduced by 53.06% to 2.126 mm using the three-channel ratio transmittance method. Compared to the MODIS/Aqua water vapor products, the RMSE has been reduced by 45.54% to 2.423 mm using the two-channel ratio transmittance method and has been reduced by 45.34% to 2.432 mm using the three-channel ratio transmittance method. Numéro de notice : A2021-338 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3016655 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3016655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97569 [article]

Les stations virtuelles au service de la cartographie mobile / Mathieu Regul in XYZ, n° 165 (décembre 2020)

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Integrated processing of ground- and space-based GPS observations: improving GPS satellite orbits observed with sparse ground networks / Wen Huang in Journal of geodesy, vol 94 n° 10 (October 2020)  

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Evaluation of single-frequency receivers for studying crustal deformation at the longitudinal Valley fault, eastern Taiwan / Horng-Yue Chen in Survey review, vol 52 n° 374 (August 2020)  

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Automated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation / Michael B. Heflin in Earth and space science, vol 7 n° 7 (July 2020)  

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Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)  

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Analyse des surcharges hydrologiques observées par géodésie spatiale avec l’outil Multi Singular Spectrum Analysis / Louis Bonhomme (2020)  

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Kalman-filter-based undifferenced cycle slip estimation in real-time precise point positioning / Pan Li in GPS solutions, vol 23 n° 4 (October 2019)  

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Processing of GNSS constellations and ground station networks using the raw observation approach / Sebastian Strasser in Journal of geodesy, vol 93 n°7 (July 2019)  

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Spatial–temporal variations of water vapor content over Ethiopia: a study using GPS observations and the ECMWF model / Kibrom Ebuy Abraha in GPS solutions, vol 21 n° 1 (January 2017)  

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On the significance of periodic signals in noise analysis of GPS station coordinates time series / Janusz Bogusz in GPS solutions, vol 20 n° 4 (October 2016)  

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